Search Results (7,394)

This paper presents a battery-aware control framework for a single-phase integrated battery charger (IBC) for electric vehicles, in which the traction system is reused as part of the charging hardware. The proposed charger consists of a stator-assisted bridgeless totem-pole power-factor-correction AC–DC stage and a bidirectional buck–boost DC–DC stage connected to a 48 kWh, 400 V lithium-ion battery pack. The battery pack is modeled using a lookup-table-based equivalent circuit model with state-of-charge- and temperature-dependent open-circuit voltage and impedance parameters. A conventional double-loop PI controller is used as the baseline, while the proposed strategy combines nonlinear model predictive control, an extended Kalman filter, and lookup-table-based battery parameterization to regulate charging current under electrical and thermal constraints. The system is evaluated under 7 kW, 230 V/32 A and 22 kW, 230 V/96 A charging cases using average-model simulations, switching-model transient simulations, and finite element thermal assessment of the induction motor stator. The average-model results show stable charging from 20% to 80% SOC, with charging times of approximately 275 min at 7 kW and 90 min at 22 kW. The EKF provides bounded battery state estimation, with maximum SOC estimation errors of approximately 1.3% and 2.0% for the 7 kW and 22 kW cases, respectively, while the core-temperature estimation error converges close to zero. The switching-model results confirm feasible duty-command behavior, bounded battery-current tracking error, and a representative DC-link ripple of approximately 8 Vpp. During grid-voltage reduction, the charging current is reduced to keep the grid-current envelope within the intended limit. FEM results show that charging-only motor temperatures remain low, reaching approximately 27.39 °C at 7 kW and 38.82–38.85 °C at 22 kW. The most critical charging-related thermal case occurs at 22 kW after one hour of full-load motor operation with a 40 °C initial condition, reaching approximately 92.32 °C. Overall, these simulation-based findings support the feasibility of the proposed NMPC–EKF–LUT framework as a battery-aware supervisory control strategy for single-phase IBC operation. The proposed controller improves constraint-aware, battery state-based decision-making, while switching ripple and motor thermal response are mainly governed by the power stage, feasible current trajectory, and initial thermal condition. Full article

Shallow lakes are vulnerable to multiple anthropogenic stressors. However, the spatial responses of benthic ecosystems to these composite disturbances and the underlying mechanisms driving them remain poorly understood. Nansi Lake is a strategic water-regulating reservoir of the Eastern Route of the South-to-North Water Transfers. It has long been subjected to multiple human activities, and its aquatic ecological environment exhibits pronounced spatial heterogeneity. A systematic assessment is thus needed to evaluate the spatial distribution patterns of surface-sediment diatom communities and their trophic response characteristics. This study integrates the Trophic Diatom Index (TDI) with multivariate statistical approaches. It analyzes the spatial distribution and driving factors of surface-sediment diatom assemblages based on diatom and water quality data from 62 sampling sites. The results reveal three distinct community zones across the lake. The first is a high-disturbance zone dominated by hydraulic regulation and mining activities. In this zone, Stephanodiscus parvus Stoermer & Håkansson is the absolute dominant species, indicating a clear eutrophic status. The second is a hydrochemically stable zone dominated by Achnanthidium minutissimum (Kützing) Czarnecki, exhibiting relatively high community integrity. The third is a vast central open-water zone characterized by the dominance of Pseudostaurosira brevistriata (Grunow) Williams & Round, representing a mesotrophic transitional state. Partial redundancy analysis (pRDA) shows that multiple explanatory variables jointly explain 28.91% of the community variation. The independent explanatory powers of anthropogenic variables (9.12%) and environmental factors (7.94%) are both higher than that of pure spatial dispersal processes (0.35%). Redundancy Analysis (RDA) indicates that different types of human activities—such as reservoir regulation, coal mining, and estuarine inflows—may influence the spatial distribution patterns of surface-sediment diatoms. They do so by jointly driving variations in lake trophic status and the ionic environment, particularly Mg²⁺ and SO₄²⁻. This study provides a scientific basis for the water resource management of shallow lakes subject to anthropogenic impacts. Full article

Over the past two decades, air-quality prediction has moved from a mainly single-model paradigm toward ensemble systems that make explicit use of diversity across models, observations, and data streams. This review connects developments that are often treated separately: chemical transport model (CTM) ensembles, tree-based and hybrid machine learning ensembles, deep learning architectures, physics-informed neural networks, and distributed approaches such as federated learning. Evidence summarized from recent systematic reviews and coordinated modeling initiatives indicates that, within comparable validation settings, ensembles often outperform individual models for PM${}_{2.5}$, PM${}_{10}$, O${}_3$, NO${}_2$, CO, and SO${}_2$ across a broad range of spatial scales and standard error metrics, including RMSE, MAE, and correlation. Operational CTM ensembles, such as the Copernicus Atmosphere Monitoring Service (CAMS) European system with eleven regional models, improve both forecast skill and uncertainty characterization for ozone and particulate matter. In data-driven applications, tree-based ensembles (Random Forest, gradient boosting, XGBoost, LightGBM) and hybrid deep architectures (CNN–LSTM models, attention-based multi-branch networks, graph neural networks) now form a core part of the state of the art for AQI (Air Quality Index) and particulate-matter estimation from structured and multi-source data. Reported performance can be very high on well-structured tabular datasets, with $R^2$ values above 0.99 in selected benchmarks and RMSE reductions of 23–45% relative to classical statistical baselines in multi-modal studies; however, these values are not directly interchangeable because pollutant type, prediction horizon, monitoring density, and validation design differ among studies. This review proposes a practical taxonomy of ensemble strategies and uses it to explain why diversity, rather than model count alone, is central to reliable air-quality prediction. Drawing on coordinated European and North American model-evaluation initiatives (AQMEII, HTAP) and on case studies in topographically and meteorologically complex Italian regions (the Po Valley, the Naples metropolitan area, and Campania), we show that effective ensemble design requires a balance among diversity, redundancy, computational feasibility, and interpretability. On the basis of a structured narrative synthesis, the main research gaps concern physics-informed and explainable ensemble frameworks, transferable and adaptive models, standardized benchmarks, severe-pollution-episode forecasting, and scalable distributed architectures. Open questions include how to design compact non-redundant CTM sub-ensembles and how to couple deep learning with chemical-transport physics in next-generation operational systems. Full article

Combinatorial optimization problems (COPs) offer essential mathematical frameworks and algorithmic foundations for modeling complex real-world decision-making tasks. Recent advances in deep reinforcement learning (DRL) have shown promising results for solving COPs, offering the potential to reduce dependence on domain-specific expertise and improve generalization across problem instances. These developments have accelerated research in the field and spurred the emergence of numerous innovative methods. Nevertheless, significant theoretical and practical challenges remain. A systematic synthesis of these challenges and their corresponding solutions is critical to guiding the future development of DRL-based approaches. To address this need, we propose a unified challenge-driven framework consisting of four core components: an environment, a state–action–reward mechanism, a solver, and an evaluation module. Using this framework, we conduct a systematic review of approximately 300 recent studies, mapping the evolution of challenges and the progress made in addressing them. We provide a multidimensional analysis of solver designs, training paradigms, and state-of-the-art (SOTA) performance, while documenting publicly available code repositories. Finally, we identify key open problems within the proposed framework to stimulate novel research directions. Full article

Standard Three-dimensional (3D) Magnetic Resonance Imaging (MRI) segmentation models typically rely on a fixed threshold or argmax-based class selection, providing little or no insight into model uncertainty, which can limit clinician trust. In this work, we developed a framework that integrates a calibrated 3D U-Net into a Virtual Reality (VR) system, enabling clinicians to manipulate confidence thresholds in real time and observe how the segmentation changes. The network was trained to segment mandibular structures (submandibular glands and mandibular canal) on 55 T2-weighted MRI scans from the AAPM RT-MAC 2019 dataset and post hoc calibrated using temperature scaling. The VR application, built in Unity with OpenXR, offers two interaction modes: a single upper-threshold mode and a range mode that controls both lower and upper bounds. A user study with six clinicians was conducted to evaluate both modes using the UMUX-Lite questionnaire (from which a System Usability Scale (SUS)-equivalent score was derived), interaction logs, and qualitative feedback. Both modes were rated as highly usable, and narrower confidence intervals, focusing on the most reliable predictions, were more popular among users. The results suggest that enabling real-time adjustment of confidence thresholds improves the clarity of segmentation outputs and may support clinicians’ perceived confidence, as indicated by participants’ qualitative feedback and stated preferences. Full article

Microwave-assisted enzymatic ring-opening polymerization (eROP) has emerged as a promising green approach for the synthesis of biomedical polymers with potential applications in drug delivery systems and implantable biomaterials. This review provides a critical overview of the current state of microwave-assisted eROP, with particular emphasis on systems catalyzed by Candida antarctica lipase B (CALB), the most widely used biocatalyst in this field. The fundamental aspects of CALB-catalyzed polymerization, including its catalytic mechanism and key parameters influencing reaction efficiency, are discussed under both conventional and microwave-assisted conditions. Microwave irradiation can offer significant advantages, such as reduced reaction times, improved energy efficiency, and, in some cases, enhanced polymer properties, including higher molecular weight and narrower dispersity. However, its effects are strongly dependent on reaction conditions, including solvent properties, temperature, and enzyme stability, with both accelerating and inhibiting effects reported. The review summarizes the current literature on microwave-assisted eROP of representative cyclic monomers, including ε-caprolactone, ω-pentadecalactone, and L-lactide, highlighting the influence of microwave parameters on polymerization outcomes and material properties. Despite promising results, microwave-assisted eROP remains relatively underexplored, with limited monomer scope and incomplete understanding of microwave–enzyme interactions. Challenges such as enzyme deactivation, lack of standardized methodologies, and scalability issues are also addressed. Overall, microwave-assisted eROP of cyclic monomers represents a sustainable and efficient strategy for producing high-quality pharmaceutical polymers. However, further research is required to fully realize its potential in industrial and biomedical applications. Full article

A transient multibody dynamics simulation of rolling-element bearings is the basis for the design of high-speed rotating machinery; however, the established solvers are proprietary, cannot be used with automatic differentiation, and offer no built-in measure of their own physical consistency. We present OpenBEARD, an open-source, fully differentiable transient dynamics solver for angular-contact ball bearings. The solver steps a $40+13Z$-component state (inner ring, cage, and Z balls with quaternion attitude, plus guide-patch, lumped-thermal, and energy-audit states) forward in time under coupled Hertzian contact, Hamrock–Dowson and full-multigrid elastohydrodynamic lubrication, thermal–elastohydrodynamic traction, and centrifugal/press-fit clearance models, using nondimensionalized implicit stiff time integration. A built-in metriplectic conservation audit checks energy closure, the second law per dissipation channel, and the gyroscopic-power identity at every output step. OpenBEARD is cross-verified against two published ADORE references of Gupta. For a high-speed NASA angular-contact ball bearing, the quasi-static contact loads, angles, stresses, and centrifugal force match the published values to within $0.3\%$, and the ball spin and orbital velocities and the spin-axis orientation to ≤0.1%. The inner-race spin-to-roll ratio—a slip-derived secondary quantity that is the most model-sensitive metric in this class of solvers—differs from the NASA quasi-static reference by $8.8\%$. In the separate caged BallBearingTestCase benchmark, the corresponding quasi-static difference is $3.2\%$, and the transient settled value is $\approx 16\%$ above the ADORE step-100 snapshot; these bounded offsets reflect different spin-moment constitutive models. The BallBearingTestCase comparison—a caged bearing under combined thrust and radial load—matches the per-ball contact angles and loads to within $0.23\%$ RMS, and a single published dynamic snapshot (step 100) agrees with the transient contact mechanics to within a few percent. The built-in energy-closure residual stays of order ${10}^{-5}$ with no second-law violations. In the fully transient regime, race control emerges as a dynamical attractor of the coupled traction balance—ball-spin states perturbed by $\pm 12\%$ converge to a single outer-race-control solution—rather than the kinematic hypothesis assumed by quasi-static theory. OpenBEARD is released under the MIT license. Full article

State estimation is a fundamental component of model-based Fault Detection and Diagnosis (FDD) in dynamic systems, underpinning real-time monitoring, predictive maintenance, and safety-critical operations across industries such as aerospace, power systems, robotics, and autonomous vehicles. Traditional estimators, including the Kalman Filter (KF) and its variants, provide physically interpretable residuals for fault detection but often fail to deliver reliable performance under nonlinear dynamics, modeling uncertainties, sensor faults, and non-Gaussian noise. This paper presents a comprehensive review of state estimation-based FDD approaches, with a particular focus on Artificial Intelligence (AI)-augmented Kalman filtering and hybrid frameworks that integrate Machine Learning (ML) models, including Neural Networks (NNs), Support Vector Machines (SVMs), and Gaussian Processes (GPs), with classical estimation theory. The review systematically evaluates model-based, data-driven, and hybrid methods, comparing their robustness, accuracy, computational efficiency, scalability, and interpretability in complex Cyber-Physical Systems (CPSs). Furthermore, emerging trends and open research challenges are identified, including online adaptation, fault-tolerant estimation, sensor fusion, explainable artificial intelligence (XAI), and deployment in Industry 4.0 and Internet of Things (IoT)-enabled environments. By bridging classical estimation theory with modern AI techniques, this review provides a roadmap for designing intelligent, adaptive, and resilient FDD systems capable of enhancing reliability, operational safety, and real-world applicability. Full article

Wasteful care is a prevalent, costly problem in the United States. Medical schools are addressing this by teaching about value-based care. However, students still struggle with the application of this concept. We hypothesized that this struggle may be due to students’ uncertainty about how to define value-based care. In this article, we evaluated a novel, interactive, value-based care session for first-year medical students and assessed students’ abilities to define value-based care pre- and post-session. Our curricular session introduced first-year medical students to value-based care principles and included both a lecture and small-group role-play exercise focused on value-based decision-making. Students completed pre- and post-session surveys that assessed how they defined value-based care, from which we identified 11 unique themes. Following the session, students provided more complete definitions. Early instruction using peer-led sessions, application through role-play, and assessment through open-ended responses were associated with improved student ability to conceptualize and define value-based care. Improved definitional clarity may allow students to better apply value-based principles to their clinical practice. However, further research is needed to assess how the ability to conceptualize this concept translates to clinical application. Full article

In remote sensing image–text retrieval (RSITR), precise cross-modal retrieval is often hindered by centroid drift and ambiguous decision boundaries caused by high inter-class visual similarity. To address these bottlenecks, this study proposes a Category-aware Global–Local Semantic Alignment (CGLSA) framework fine-tuned on the CLIP (ViT-B/16) backbone. The architecture orchestrates two complementary mechanisms: Global Semantic Collaborative Alignment that regularizes macro-level category centroids using momentum updates and Bayesian prior calibration, and Local Fine-grained Feature Alignment that refines instance-level matching via dynamic scale adjustment and category-aware topological masks. Extensive evaluations on three major benchmark datasets (RSICD, RSITMD, and UCM-Captions) validate the model’s efficacy. Compared to strictly controlled CLIP-family baselines under equivalent supervised conditions, CGLSA achieves new state-of-the-art performance across all R@K metrics and mean recall. Extensions adapting this robust centroid formation to semi-supervised and open-vocabulary scenarios are identified for future work. Full article

Achieving scalable swarm autonomy in Global Navigation Satellite System (GNSS)-denied and communication-constrained environments remains an open challenge at the intersection of robotics, distributed optimization, and reinforcement learning. Existing unmanned aerial vehicle (UAV) autonomy frameworks typically decouple navigation, perception, and distributed learning, while assuming centralized coordination or reliable global positioning. This paper introduces a unified federated deep reinforcement learning architecture that enables GNSS-independent multi-UAV autonomy through the principled integration of multi-modal perception, decentralized policy optimization, energy-aware control, and edge-compliant inference. The proposed framework formulates joint navigation and dynamic target tracking as a partially observable Markov decision process optimized via Proximal Policy Optimization (PPO) over structured motion primitives. A communication-efficient federated learning mechanism enables distributed policy convergence under non-independent and identically distributed (non-IID) agent experiences without sharing raw data, establishing a scalable alternative to centralized training. To address sim-to-real discrepancies, the architecture incorporates domain randomization, structured sensor noise modeling, and curriculum-based training to promote robust zero-shot deployment. Multi-agent simulation experiments evaluate the swarm-level and federated-learning behavior of the proposed framework, while single-UAV field deployment evidence using a DJI Matrice 100 platform supports the feasibility of the onboard sensing, perception, and edge-inference pipeline under realistic outdoor conditions. The evaluation demonstrates stable decentralized convergence, improved energy efficiency relative to centralized baselines, robust target-tracking performance under GNSS-denied conditions, and real-time edge-compliant inference. The results establish that federated reinforcement learning can serve as a viable systems-level foundation for resilient, energy-aware, and scalable aerial swarm intelligence, advancing the state of the art in distributed autonomous robotics. Full article

Remote sensing vision–language models, such as RemoteCLIP and GeoRSCLIP, have advanced image–text representation learning. However, they rely on manually curated caption datasets that are expensive to scale and provide only global image-level supervision. In this paper, we introduce OSM-CLIP, a framework that exploits the freely available, continuously growing annotations of OpenStreetMap (OSM) to provide regionally scalable, patch-level supervision for remote sensing image-text learning. We construct a large-scale dataset of over 265,000 satellite images covering the contiguous United States, each automatically paired with fine-grained geographic annotations scraped from OSM and mapped to individual image patches. A contrastive loss operating at the patch level associates each image region with its corresponding OSM textual description, enabling the model to learn spatially grounded representations without any manual labeling effort. After fine-tuning on standard remote sensing captioning datasets, OSM-CLIP achieves an average improvement of 10.81% in zero-shot classification, 5.06% in text-to-image retrieval (R@1), and 3.87% in image-to-text retrieval (R@1) over existing methods across 13 classification and 4 retrieval benchmarks. Our results demonstrate that freely available geographic annotations can serve as a powerful source of supervision for remote sensing vision–language models in regions with high-quality OSM coverage. Full article

Non-stoichiometric tungsten carbide WC_1−x layers (~20 mm thickness) were used as a reductant substrate to deposit palladium nanoparticles under open-circuit conditions in aqueous solutions. The prepared Pd(WC_1−x)/TEG electrodes (TEG—thermally expanded graphite) were studied by a complex of modern physical methods: scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD) and Raman Spectroscopy. The reduction of Pd(II) species to the metallic state was confirmed by XPS and XRD. The coherent scattering domain (CSD) for Pd particles was determined using the Scherrer formula (peak broadening analysis) with the LaBeil profile fitting the XRD pattern. It was ca. 16 nm, which would correspond to a specific surface area of palladium of 30.9 m²·g⁻¹, assuming a spherical shape of its particles. According to electrochemical studies, the specific area of palladium is significantly lower (8.5 m²·g⁻¹ in 0.1 M PdCl₂ after 2 h of deposition), which is due to the inevitable coalescence of metal nanoparticles during the currentless deposition process. Pd(WC_1−x) composites demonstrated high catalytic activity in the hydrogen evolution reaction (HER) and formic acid oxidation reaction (FAOR). Thus, currentless deposition can be considered as a relatively simple method for producing electrode catalysts. Full article

Electrode-particle fracture and thermal runaway remain major safety and durability challenges for lithium-ion batteries. Deterministic degradation models are limited in representing random crack nucleation, long-range crack interactions, and critical transitions from stable operation to failure. A computational framework is proposed that combines a Lévy-jump-driven nonlocal stochastic partial differential equation (SPDE) model with a Bifurcation-Aware Physics-Informed Neural Network (BA-PINN). The framework couples fractional diffusion, peridynamic damage evolution, thermal feedback, state-space eigenvalue tracking, and damage-variance monitoring. Evaluation is conducted on controlled synthetic fracture simulations, Oxford battery cycling records, and open-access abuse-test records from the Battery Failure Databank. The damage-field results are interpreted as numerical consistency and surrogate-learning evidence, with direct experimental crack-map validation remaining outside the present dataset scope. On the simulated fracture dataset, the proposed method obtains a damage-field mean squared error of 0.023 ± 0.002 and a structural similarity index of 0.962 ± 0.006. For the evaluated thermal-runaway warning task, it achieves an AUC-ROC of 0.987 ± 0.004 and an average model-inferred warning lead time of 5.2 ± 0.2 h. These results demonstrate the methodological feasibility of combining stochastic nonlocal fracture modeling with bifurcation-aware learning. However, broader validation remains necessary, particularly using particle-resolved experiments and larger event-level thermal-runaway datasets. Full article

Background/Objectives: This national database study compares wide-awake hand surgeries in the United States performed in the operating room and office in terms of volume, reimbursement, narcotics prescriptions, and adverse events. Methods: Patients who underwent trigger finger release, open carpal tunnel release, De Quervain’s release, and mucous cyst excision from 2010 to 2022 were identified in PearlDiver’s M170Ortho dataset. Exclusion criteria were concomitant hand surgery, inpatient setting, <30 days of follow-up, age < 18 years, general/monitored anesthesia, and nerve block. Cohorts were stratified by surgical setting then matched by age, sex, Elixhauser Comorbidity Index score, and region. Primary endpoints included total and physician reimbursement (stratified by payor: commercial, Medicaid, Medicare) and 90-day narcotic prescriptions, emergency department visits, and surgical site infections. Results: Between 2010 and 2022, all surgical cohorts demonstrated an increase in the annual proportion of surgeries performed in the office (trigger finger +36%, carpal tunnel +155%, De Quervain’s +104%, mucous cyst +22%). Office-based surgery demonstrated lower total costs for all surgical cohorts and insurance types: commercial (−34% to −43%), Medicaid (−37% to −48%), Medicare (−30% to −37%). Office-based surgery had lower physician reimbursement for all surgical cohorts with commercial insurance (−3% to −9%) and Medicare (−5% to −13%). Physician reimbursement was not significantly different by surgical setting for Medicaid patients. Following office-based surgery, patients filled fewer narcotic prescriptions and had lower rates of emergency department visits, with similar rates of surgical site infection. Conclusions: Although most wide-awake hand surgeries are still performed in the operating room, there is a nationwide increase in office-based surgery. With reduced financial burden and favorable rates of adverse events, office-based wide-awake hand surgery may offer improved economic value and comparable short-term safety for certain procedures. Future research should complement these findings with patient-reported outcomes. Full article